Non-physician providers of obstetric care in Mexico: Perspectives of physicians, obstetric nurses and professional midwives
Bibliographic record
Abstract
BACKGROUND: In Mexico 87% of births are attended by physicians. However, the decline in the national maternal mortality rate has been slower than expected. The Mexican Ministry of Health's 2009 strategy to reduce maternal mortality gives a role to two non-physician models that meet criteria for skilled attendants: obstetric nurses and professional midwives. This study compares and contrasts these two provider types with the medical model, analyzing perspectives on their respective training, scope of practice, and also their perception and/or experiences with integration into the public system as skilled birth attendants. METHODOLOGY: This paper synthesizes qualitative research that was obtained as a component of the quantitative and qualitative study that evaluated three models of obstetric care: professional midwives (PM), obstetric nurses (ON) and general physicians (GP). A total of 27 individual interviews using a semi-structured guide were carried out with PMs, ONs, GPs and specialists. Interviews were transcribed following the principles of grounded theory, codes and categories were created as they emerged from the data. We analyzed data in ATLAS.ti. RESULTS: All provider types interviewed expressed confidence in their professional training and acknowledge that both professional midwives and obstetric nurses have the necessary skills and knowledge to care for women during normal pregnancy and childbirth. The three types of providers recognize limits to their practice, namely in the area of managing complications.We found differences in how each type of practitioner perceived the concept and process of birth and their role in this process. The barriers to incorporation as a model to attend birth faced by PMs and ONs are at the individual, hospital and system level. GPs question their ability and training to handle deliveries, in particular those that become complicated, and the professional midwifery model particularly as it relates to a clinical setting, is also questioned. CONCLUSIONS: Hospitals in the Mexican public health sector have a heavy obstetric workload; physicians carry the additional burden of non-obstetric cases. The incorporation of a non- physician model at the primary health center level to attend low-risk, normal deliveries would contribute to the reduction of non-necessary referrals. There is also a role for these providers at the hospital level.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".